Detecting, Interpreting and Modifying the Heterogeneous Causal Network in Multi-Source Event Sequences

Uncovering causal relations from event sequences to guide decision-making has become an essential task across various domains. Unfortunately, this task remains a challenge because real-world event sequences are usually collected from multiple sources. Most existing works are specifically designed for homogeneous causal analysis between events from a single source, without considering cross-source causality. In this work, we propose a heterogeneous causal analysis algorithm to detect the heterogeneous causal network between high-level events in multi-source event sequences while preserving the causal semantic relationships between diverse data sources. Additionally, the flexibility of our algorithm allows to incorporate high-level event similarity into learning model and provides a fuzzy modification mechanism. Based on the algorithm, we further propose a visual analytics framework that supports interpreting the causal network at three granularities and offers a multi-granularity modification mechanism to incorporate user feedback efficiently. We evaluate the accuracy of our algorithm through an experimental study, illustrate the usefulness of our system through a case study, and demonstrate the efficiency of our modification mechanisms through a user study.